(2024) 22:312
Gunsaru et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03532-7

BMC Medicine

RESEARCH

Open Access

The impact of the COVID‑19 pandemic
on tuberculosis treatment outcomes in 49 highburden countries
Vester Gunsaru1* , Marc Y. R. Henrion1,2   and C. Finn McQuaid3   

Abstract
Background The COVID-19 pandemic disrupted tuberculosis (TB) health services, including treatment support
and access to drugs, as patients were not able to access health facilities. While the effect of this disruption on treatment outcomes has been studied in isolated treatment centres, cities and provinces, the impact of the pandemic
on TB treatment outcomes at a country and regional level has not been evaluated.
Methods We used treatment outcomes for new and relapse TB cases reported to the World Health Organization(WHO) from 49 high TB, TB/HIV and drug-resistant TB burden countries from 2012 to 2019. We developed multinomial
logistic regression models for trends in TB treatment success, failure, death and loss to follow up. We predicted TB
treatment outcomes for 2020 and 2021, comparing these to observations, by computing ratios between observed
and predicted probabilities. We aggregated these risk ratios (RR) for six WHO-defined regions using random-effects
meta-analysis.
Results Across 49 countries and four TB treatment outcomes, 17 (out of 196) country-outcome pairs in 2020 and 21
in 2021 had evidence of systematic differences between observed and predicted TB treatment outcome probabilities.
Regionally, only four (out of 24) region-outcome pairs had evidence of systematic differences in 2020 and four in 2021,
where the European region accounted for four of these in total. Globally, there was evidence of systematic differences in treatment failure in both 2020 (RR: 1.14, 95%CI: 1.01–1.28, p = 0.0381) and 2021 (RR: 1.36, 95%CI: 1.03–1.78,
p = 0.0277), deaths in 2020 (RR: 1.08, 95%CI: 1.03–1.13, p = 0.0010) and losses to follow up in 2020 (RR: 0.91, 95%CI:
0.86–0.97, p = 0.0059).
Conclusions While for some countries and regions there were significant differences between observed and predicted treatment outcomes probabilities, there was insufficient evidence globally to identify systematic differences
between observed and expected TB treatment outcome probabilities because of COVID-19-associated disruptions
in general. However, larger numbers of treatment failures and deaths on treatment than expected were observed
globally, suggesting a need for further investigation.
Keywords Tuberculosis, COVID-19, Treatment, Outcomes*Correspondence:
Vester Gunsaru
vgunsaru@mlw.mw
1
Malawi Liverpool Wellcome Programme, Blantyre, Malawi
2Liverpool School of Tropical Medicine, Liverpool, UK
3
TB Modelling Group, TB Centre and Centre for Mathematical Modellingof Infectious Diseases, Department of Infectious Disease Epidemiology,
Faculty of Epidemiology and Population Health, London Schoolof Hygiene & Tropical Medicine, London, UK© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Gunsaru et al. BMC Medicine(2024) 22:312

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Background
The COVID-19 pandemic resulted in major disruptions
to tuberculosis (TB) diagnosis and prevention [1–3]. In
particular, a significant decrease was seen globally inthe number of people with TB who were able to accessdiagnosis and were reported to the health system, with
1.3 million fewer individuals notified in 2020 than 2019[1]. Over the same period 700,000 fewer individuals
accessed TB preventive treatment [1], primarily people
living with HIV. In 2022, the world started recoveringfrom these disruptions, with 1.1 million more notified
cases than 2021, and 1.7 million more notified cases
than 2020 [1].
At the same time, the pandemic disrupted access to
TB treatment. Patients experienced delays to treatment
initiation, medication stock-outs and lack of access,and disruptions to treatment support [4], with potential
interruptions to treatment adherence as a consequence.
Poor adherence to TB treatment itself, whether starting
treatment late, ending treatment early, or intermittently
missing doses [5], can lead to poor treatment outcomes, including disease relapse and drug-resistance[6, 7]. Consequently, these interruptions to TB treatment could potentially have major repercussions for
TB treatment outcomes, potentially reducing treatment
success, and increasing treatment failure, loss to follow
up and death on treatment.
Previous studies in high TB burden settings indicate a mixture of evidence for this. Smaller studies inindividual treatment centres, cities and provinces in
China [8], Ethiopia [9, 10], Zimbabwe [11, 12], Eswatini[13], India [14–16], Kazakhstan [17], Uganda [18] andMexico [19] suggest that treatment outcomes may havedeteriorated. However, a handful of other small studies in Kenya [12, 20], Malawi [12, 21], Sierra Leone [22]and Indonesia [23], as well as much larger studies inLesotho [24], Indonesia [25], Peru [26], Brazil [27] andVietnam [28], do not appear to have experienced thisdeterioration. More recent evidence suggests that at a
global level treatment outcomes do not appear to havebeen affected by the pandemic [1]. However, this may
mask significant variations at a country level as well as
sub-nationally.
(t − τk )3∗ = (t − τk )3+ − (t − τK −1 )3+

in treatment failure, relapse and death on treatment, as
compared to expected values.

Methods
Data

We used country level treatment outcome data for
new and relapse drug-susceptible TB cases reported to
the World Health Organization (WHO) by individual
WHO region countries. These were data recorded on
TB treatment success, failure, death and loss to follow
up for the years 2012 – 2021 [29]. We focused on WHO
high TB, TB/HIV and multidrug/rifampicin-resistant
TB (MDR/RRTB) burden countries [30].
Analysis

We fitted multinomial logistic regression (MLR) models
to data from 2012–2019 to estimate the expected probabilities of TB outcomes in 2020 and 2021 for each country. The MLR model included time t (t = 2012, 2013,…,2019) as a predictor variable. We used natural cubic
regression splines to model the non-linear relationshipbetween TB treatment outcomes and time with K knots.
Natural splines were used due to having linear functionsin the boundary knots tails which provided more stablemodel fits. The probability of each level j of Y , the TB
treatment outcome for country c was then given by:
Pr Ycjt = Pr Yc = j t) =eβocj +β1cj t+
1+

K −2
3
k=1 βkcj (t−τk )∗J −1 β0cl +β1cl t+
l=1 e

K −2
3
k=1 βkcl (t−τk )∗for j = 1, …, J-1, and for the last level J:
Pr YcJt = Pr(Yc = J |t) =1+1
J −1 β +β t+K −2 β (t−τ )3
0cl
1cl
k ∗
k=1 kclel=1

The natural logarithm of the probability then gives:lnPr(Ycjt )Pr(YcJt )= βocj + β1cj t +

K −2

3
k=1 βkcj (t − τk )∗ , j = 1, . . . , J

Where τk are t values at knot k and for k = 1, …, K − 2,

τK − τkτK −1 − τk+ (t − τK )3+,
τK − τK −1
τK − τK −1

Here we investigated whether disruptions to TB
treatment resulted in worsening treatment outcomes in
49 high TB burden countries and globally. We hypothesized that COVID-19-associated disruptions would
lead to a decrease in treatment success and an increase(t − τk )+ = ct − τk : if t − τk > 0
0 : if t − τk ≤ 0

Once the models were fitted, we computed the modelpredicted treatment outcome probabilities for 2020 andGunsaru et al. BMC Medicine(2024) 22:312

Page 3 of 10

2021 and compared these to the observed outcome proportions for 2020 and 2021 for each country, by computing the ratio between the observed and the expectedvalues. Bootstrap sampling was used to derive confidence
intervals for the estimated outcome probabilities, and, inturn, the ratios between observed and expected proportions and their corresponding p-values.
To pool the effect sizes, and given the diverse set ofcountries in our dataset, we conducted random-effects
meta-analysis of the ratios between observed and predicted outcome probabilities, overall and by WHO
region. We estimated the percentage of the variation
across studies not due to sampling error using the I2
statistic. Restricted maximum-likelihood estimator wasused to estimate between-country variance. All analyses
were done in the R (version 4.3.1) environment for statistical computing [31–44].
We considered there to be evidence of systematic differences between estimated and observed treatment success probabilities if the p-value for the ratio was < 0.05.

Results
We included data from 49 high TB, TB/HIV and MDR/RRTB burden countries that were reported to the WHOfrom 2012–2021 (Kazakhstan and Uzbekistan did nothave data for 2021). The 49 countries included 24 fromthe WHO African region, nine from the European
region, seven from South-East Asia region, two from theRegion of the Americas, five from Western Pacific region
and two from the Eastern Mediterranean region. The
observed probability of TB treatment success aggregatedacross all countries indicated a general increase from2012 to 2021. A similar general decrease was seen fortreatment failure and loss -to -follow up. Observed proportions of deaths fluctuated over time (Table 1).
Comparing the observed and estimated TB treatment
success from the MLR models in 2020 (Fig. 1), the Central
African Republic (1.06, 95% CI: 1.01–1.39, p = 0.0259),
Guinea-Bissau (1.06, 95% CI: 1.01–1.22, p = 0.0339),
Pakistan (1.01, 95% CI: 1.00–1.06, p = 0.0218), the Russian Federation (0.93, 95% CI: 0.87–0.98, p = 0.0338) and
Somalia (1.01, 95% CI: 1.00–1.05, p = 0.0143) had statistically significant ratios, i.e. there were more observed

successes than expected in all countries except in the
Russian Federation where there were fewer. For 2021
(Additional file 1: Fig. S1), Bangladesh (1.01, 95% CI:
1.00–1.02, p = 0.0468), the Central African Republic(1.06, 95% CI: 1.00–1.71, p = 0.0434), Pakistan (1.02, 95%
CI: 1.01–1.11, p = 0.0116), the Philippines (0.98, 95%
CI: 0.96–0.99, p = 0.0339) and Zimbabwe (1.03, 95% CI:
1.00–1.35, p = 0.0240) had statistically significant ratios,
i.e. there were more observed successes than expected in
all other countries except in the Philippines where there
were fewer.
Comparing observed and estimated TB treatment failure in 2020, statistically significant ratios were observedin Angola (1.90, 95% CI: 1.06–7.19, p = 0.0450), Congo(2.59, 95% CI: 1.26–55.18, p = 0.0232), Eswatini (10.03,
95% CI: 3.62–40.54, p = 0.0052) and the Russian Federation (1.66, 95% CI: 1.15–2.37, p = 0.0229), i.e. there weremore observed failures than expected in these countries.
For 2021, Congo (5.54, 95% CI: 1.91–651.18, p = 0.0137),
Nigeria (16.68, 95% CI: 6.43–24.13, p = 0.0015), the Russian Federation (1.70, 95% CI: 1.05–3.01, p = 0.0352) and
Viet Nam (2.03, 95% CI: 1.21–32.49, p = 0.0249) had statistically significant ratios in treatment failure, i.e. there
were more observed failures than expected in thesecountries.
Comparing observed and expected TB deaths in
2020, statistically significant ratios were observed in
Brazil (1.15, 95% CI: 1.08–1.30, p = 0.0180), GuineaBissau (0.67, 95% CI: 0.45–0.96, p = 0.0458), Indonesia(1.63, 95% CI: 1.36–2.44, p = 0.0022), Philippines (1.29,
95% CI: 1.08–1.66, p = 0.0337), the Russian Federation (1.20, 95% CI: 1.07–1.29, p = 0.0140) and Tajikistan(1.40, 95%CI: 1.04-1.77, p = 0.0159), i.e. there were more
observed deaths than expected in these countries except
in Guinea Bissau where there were fewer. For 2021, Brazil (1.26, 95% CI: 1.13–1.51, p = 0.0153), Eswatini (2.30,
95% CI: 1.05–6.18, p = 0.0467), Guinea-Bissau (0.39,
95% CI: 0.24–0.71, p = 0.0208), Indonesia (1.70, 95%
CI: 1.25–3.14, p = 0.0064), Mozambique (1.71, 95% CI:
1.04-2.30, p = 0.0422), Nigeria (0.09, 95% CI: 0.03–0.13,
p = 0.0017), the Russian Federation (1.24, 95% CI: 1.06–
1.37, p = 0.0188) and Ukraine (1.27, 95% CI: 1.03–1.72,
p = 0.0315) had statistically significant ratios in TB

Table 1 Probabilities of TB treatment outcomes by year (2012–2021) aggregated across 49 high TB, TB/HIV and MDR/RR-TB burdencountries, as a weighted average of country-specific outcomes
2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Success

0.90430.90630.90720.91020.90380.91100.90610.9111

0.91280.9176

Failure0.01180.01110.00960.00920.01310.00920.00760.00730.00720.0074Death

0.03980.03910.03920.03910.04070.03790.03690.03870.04200.0394

Loss to follow up

0.0441

0.0436

0.04390.04140.04240.04190.04930.04280.03800.0356

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Fig. 1 Observed and expected tuberculosis treatment outcome probabilities for 49 high TB, TB/HIV and MDR/RR-TB burden countries in 2020.
Labelled are those with statistically significant differences: AGO – Angola, BRA – Brazil, CAF – Central African Republic, COG – Congo, GNB –Guinea-Bissau, IDN – Indonesia, KGZ – Kyrgyzstan, PAK – Pakistan, PHL – Philippines, SOM – Somalia, SWZ- Eswatini, TJK—Tajikistan and RUS –the Russian Federation

deaths. There were more observed deaths than expected
in Brazil, Indonesia, Mozambique, the Russian Federation and Ukraine, and less observed deaths than expected
in Guinea-Bissau and Nigeria.
Comparing observed and expected loss to follow up
in 2020, statistically significant ratios were observed in
Kyrgyzstan (0.79, 95% CI: 0.45–0.93, p = 0.0322) andPakistan (0.72, 95% CI: 0.48–0.94, p = 0.0413), i.e. there
were less observed losses to follow up than expected.
For 2021, Belarus (5.76, 95% CI: 1.25–17.93, p = 0.0348),
Pakistan (0.60, 95% CI: 0.35–0.96, p = 0.0434), the Philippines (1.45, 95% CI: 1.12–2.48, p = 0.0307) and the Russian Federation (0.52, 95% CI: 0.46-0.96, p = 0.0432) had
statistically significant ratios in losses to follow up. There
were more observed losses to follow up than expectedin Belarus and the Philippines, and less observed losses
to follow up than expected in Pakistan and the RussianFederation.
See Additional file 1: Tables S1, S2, S3, S4, S5, S6, S7,
S8, S9, S10, S11, S12, S13, S14 for further details on differences between observed and expected TB treatment
outcomes in 2020 and 2021.
Meta‑analyses

Pooled effects for TB treatment success at WHO
regional level showed evidence of systematic differences between observed and expected probabilities in2020 (Fig. 2a) in the European Region (0.97, 95% CI:
0.94-0.99, p = 0.0140, I 2 =0%), and in 2021 (Additional

Gunsaru et al. BMC Medicine(2024) 22:312

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Fig. 2 Random effect meta-analyses forest plots highlighting ratios between observed and expected proportions for tuberculosis treatment
success (a) and failure (b) in 2020 for 49 high TB, TB/HIV and drug resistant TB burden countries by WHO region. Greater than one ratio imply
that the observed proportions were more than expected, and less than one ratio imply that the observed proportions were less than the expected

file 1: Fig. S2a) in the European Region (0.94, 95% CI:
0.90-0.99, p = 0.0103, I 2 = 0%) and the Western PacificRegion (0.98, 95% CI: 0.97-1.00, p = 0.0153, I 2 = 0%).
However, there was no evidence of systematic differences globally both in 2020 and 2021.

There was no evidence of systematic differences fortreatment failure in any of the regions but there wasevidence of systematic differences globally in both 2020
(1.14, 95% CI: 1.01-1.28, p = 0.0381, I 2 = 0%) (Fig. 2b)and 2021 (1.36, 95% CI: 1.03-1.78, p = 0.0277, I 2 = 43.7%)(Additional file 1: Fig. S2b).

Gunsaru et al. BMC Medicine(2024) 22:312

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Fig. 3 Random effect meta-analyses forest plots highlighting ratios between observed and expected proportions for tuberculosis treatment death(a) and loss to follow up (LTFU) (b) in 2020 for 49 high TB, TB/HIV and drug resistant TB burden countries by WHO region. Greater than one ratio
imply that the observed proportions were more than expected, and less than one ratio imply that the observed proportions were less than the
expected

There was evidence of systematic differences for TB
deaths in 2020 (Fig. 3a) in the European Region (1.21,
95% CI: 1.12-1.30, p = 1.75e-06, I 2 = 0%) and the Regionof the Americas (1.14, 95% CI: 1.05-1.25, p = 0.0036, I 2 =0%), and in 2021 (Additional file 1: Fig. S3a) in theEuropean Region (1.26, 95% CI: 1.13-1.40, p = 3.07e05, I 2 = 0%) and the Region of the Americas (1.24, 95%
CI: 1.08-1.43, p = 0.0027, I 2 = 0%). There was also evidence of systematic differences globally (1.08, 95% CI:
1.03-1.13, p = 0.0010, I 2 = 2.0%) in 2020.

Gunsaru et al. BMC Medicine(2024) 22:312

Lastly, there was evidence of systematic differences forloss to follow up in 2020 (Fig.3b) in the Eastern Mediterranean Region (0.70, 95% CI: 0.51-0.96, p = 0.0265, I 2 =0%) and globally (0.91, 95% CI: 0.86-0.97, p = 0.0059, I 2 =0%). There was no evidence of systematic differences in
2021 (Additional file 1: Fig. S3b).

Discussion
Across 49 countries and two years, statistically significantly more treatment successes were observed than
expected in eight countries and fewer treatment successes were observed than expected in two countries.
Significantly more treatment failures than expected wereobserved in eight countries, while more deaths than
expected were observed in 11 countries and less deaths
than expected were observed in three countries acrossthe two years. Statistically significantly more losses tofollow up than expected were observed in two countries
and less losses to follow up than expected were observedin four countries across 2020 and 2021. Regionally, only
four (out of 24) region-outcome pairs had evidence ofsystematic differences in 2020 and four in 2021, where
the European region accounted for four of these in total.
Globally, there was evidence of systematic differences in
the failure outcome in both 2020 and 2021, death outcome in 2020 and loss to follow up in 2020.
Our results are limited by the fact that treatment outcomes (where treatment success is defined as a combination of cure and treatment completion [29]), arecomparatively blunt indicators. For example, it may bethat patients were considered to have completed treatment six months after enrollment, without receiving
a negative bacteriological test or ingesting the recommended number of drugs. An increase in the proportion of treatment success that was a result of treatmentcompletion may mask a decrease in patients who wereactually cured of TB. The significant observed reduction
in TB diagnosis [1] could also have resulted in those who
were at an increased risk of poor treatment outcomes notaccessing diagnosis, such that the populations we compare before and during the pandemic may be significantly
different. While studies have identified groups with differential treatment outcomes potentially at an increased
risk of missed or delayed diagnosis [45], other risk factors
not included in routine reporting could also play a role
here. Indeed, due to the structure of reporting systems
there is a possibility that any increase in death seen in our
results is a consequence of an increase in death due toother causes (in particular, COVID-19), rather than TB.
This is particularly likely in countries where the population with TB is at an increased risk, such as older populations or populations with multiple comorbidities. These
outcomes also represent an aggregation of all patients

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in a country who received treatment across an entire
year. It is not possible to identify whether those who
did so during stringent lockdowns may have been more
affected than those who received treatment during periods where lockdowns were less stringent or non-existent.
Similarly, those treated in 2021 may represent individuals who experienced a delayed diagnosis, and potentially therefore more severe disease, in 2020 as a result of
COVID-19-assocaited disruptions. This increase in disease severity could have affected treatment outcomes in
2021, and may still for future years, however it is not possible to disaggregate these results here. It is also not possible to identify variation at a more granular geographic
scale, where previous centre-, city-, and province-specificstudies suggest that this could be important. At the sametime, there are limitations in the data available for analysis, where we considered just one predictor (time) withno other available factors that may have changed during
the study period and that may have played a role in TB
treatment outcomes across different countries. Lastly,
our analysis focuses on drug-susceptible TB; given the
differences in care between drug susceptible and MDR/RR-TB, such as drug-susceptibility monitoring andextended duration of treatment, it would be inadvisable
to extrapolate our findings to MDR/RR-TB.
Our results are in line with a number of other countrylevel studies in finding that, unlike widespread reductions in TB diagnosis and prevention, at a country level
TB treatment outcomes do not appear to have been
noticeably affected by disruptions associated with the
pandemic. In particular, treatment success remained
high, and loss to follow up and relapse were not widely
affected. It is likely that the pandemic saw an increase inrisk factors associated with poor TB treatment outcomes,
such as untreated HIV [1], undernourishment [46], poverty [47], alcohol use [48] and many others [49]. In thiscontext, given the individual-level consequences of poor
treatment outcomes including TB relapse, lung damage and death, this achievement should be celebrated. A
widespread switch to digital treatment adherence technologies to support treatment [50], as well as significant efforts on the part of healthcare workers to ensure
patients were able to access medicines [51], may havehelped to reduce the effect of these disruptions, potentially including a reduction in loss to follow up.
However, where we did observe a change in TB treatment outcomes, this was broadly a decrease in treatment
success and an increase in poor treatment outcomes.
A small number of countries, in particular Eswatini,
Guinea-Bissau, Nigeria, Pakistan, Philippines, and theRussian Federation, also appeared to be more affected
than others with multiple indicators affected, warranting further investigation. For example, in Nigeria thisGunsaru et al. BMC Medicine(2024) 22:312

may represent changes due a recent concerted effort toincrease case finding during the study period, rather thanas a result of COVID-19-assocaited disruptions. Most
strikingly, evidence from our analysis that TB treatment
failure and deaths on treatment may have increased in2020 requires further study, although a relatively small
effect size suggests that there may not be cause for serious concern. Overall, therefore, although we foundlimited evidence at a country level that TB treatment
outcomes worsened as a result of the pandemic, we didfind some evidence at a global level that certain pooroutcomes increased. As a result, fears that potentiallythousands more people treated for TB might experiencelong-term consequences as a result of pandemic-associated disruptions appear to warrant further investigation.

Conclusions
Our study found limited evidence of COVID-19 associated disruptions on TB treatment outcomes in high burden countries and globally. Some countries registered
fewer treatment successes than expected and more failures, deaths and losses to follow up than expected. Globally, however, there were more treatment failures anddeaths than expected in 2020, but less losses to follow up.
Given these findings, in a future pandemic, it is crucial
that TB treatment outcomes are closely monitored sothat, where necessary, TB treatment initiatives and campaigns may be intensified to achieve better outcomes.
AbbreviationsCI	95% Confidence intervalHIV	Human immunodeficiency virus
MDR/RR-TB	Multidrug- or rifampicin-resistant tuberculosis
MLR	Multinomial logistic regression
TB	TuberculosisWHO	World Health Organization

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03532-7.
Additional file 1: Figures S1-S3 and Tables S1-S14. Fig. S1 – Country specificobserved and expected tuberculosis treatment probabilities in 2021. Fig.
S2 – Meta-analyses forest plots for successes and failures in 2021. Fig.
S3 – Meta analyses forest plots for deaths and losses to follow up in 2021.
Table S1 – WHO region specific observed and expected tuberculosis treatment outcomes in 2020. Table S2 – WHO region specific observed andexpected tuberculosis treatment outcomes in 2021. Table S3 – Country
specific observed and expected tuberculosis treatment outcomes for the
African region in 2020. Table S4 – Country specific observed and expected
tuberculosis treatment outcomes for the European region in 2020.
Table S5 – Country specific observed and expected tuberculosis treatment outcomes for the South-East Asia region in 2020. Table S6 – Country
specific observed and expected tuberculosis treatment outcomes forthe region of the Americas in 2020. Table S7 – Country specific observed
and expected tuberculosis treatment outcomes for the Western Pacificregion in 2020. Table S8 – Country specific observed and expected
tuberculosis treatment outcomes for the Eastern Mediterranean regionin 2020. Table S9 – Country specific observed and expected tuberculosis

Page 8 of 10

treatment outcomes for the African region in 2021. Table S10 – Country
specific observed and expected tuberculosis treatment outcomes for
the European region in 2021. Table S11 – Country specific observed andexpected tuberculosis treatment outcomes for the South-East Asia regionin 2021. Table S12 – Country specific observed and expected tuberculosis
treatment outcomes for the region of the Americas in 2021. Table S13– Country specific observed and expected tuberculosis treatment outcomes for the Western Pacific region in 2021. Table S14 – Country specificobserved and expected tuberculosis treatment outcomes for the Eastern
Mediterranean region in 2021.
Acknowledgements
Not applicable.
Authors’ contributions
VG and CFM conceived and designed the study. VG and MYRH performed theanalysis. VG wrote a first draft of the article. VG, MYRH and CFM designed themethodology, critiqued the results and contributed to editing the final draft.
All authors read and approved the final manuscript.
Authors’ Twitter handles
MYRH—@lemoncul.
CFM—@cfmcquaid.
Funding
This research was funded in whole, or in part, by the Wellcome Trust[grant: 206545/Z/17/Z]. CFM was funded for other work by BMGF (TB
MAC OPP1135288, INV-059518), NIH (R-202309–71190), and Unitaid(20193–3-ASCENT). For the purpose of open access, the authors have applied
a CC BY public copyright license to any Author Accepted Manuscript version
arising from this submission. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Availability of data and materials
All data generated or analysed during this study are included in this publishedarticle and its supplementary files or through the World Health Organization
repository: https://​www.​who.​int/​tb/​count​ry/​data/​downl​oad/​en/. The analysis
code can be accessed via https://​github.​com/​Veste​r123/​TB-​COVID-​19-​Manus​
cript.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 1 March 2024 Accepted: 16 July 2024

References
1. World Health Organization. Global tuberculosis report 2023. Geneva;
2023. Available from:https://​www.​who.​int/​teams/​global-​tuber​culos​is-​progr​amme/​tb-​repor​ts.
2. McQuaid CF, Vassall A, Cohen T, Fiekert K. Covid/Tb Modelling Working
Group*, White RG The impact of COVID-19 on TB: a review of the data. Int
J Tuberc Lung Dis. 2021;25(6):436–46.
3. Migliori GB, Thong PM, Alffenaar JW, Denholm J, Tadolini M, AlyaquobiF, et al. Gauging the impact of the COVID-19 pandemic on tuberculosis
services: a global study. Eur Respir J. 2021;58(5):2101786.

Gunsaru et al. BMC Medicine

4.
5.
6.
7.

8.
9.
10.

11.

12.
13.
14.
15.

16.

17.

18.
19.

20.

21.

22.
23.

(2024) 22:312

Stop TB Partnership. The impact of COVID-19 on the TB epidemic: A community perspective. Available from: https://​www.​stoptb.​org/​file/​9318/​
downl​oad
Stagg HR, Flook M, Martinecz A, Kielmann K, Zur Wiesch PA, Karat AS, et al.
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era. ERJ Open Res. 2020;6(4):00315–2020.
Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar JW. Risk factors ofmultidrug-resistant tuberculosis: A global systematic review and metaanalysis. J Infect. 2018;77(6):469–78.
Chimeh RA, Gafar F, Pradipta IS, Akkerman OW, Hak E, Alffenaar JWC,
et al. Clinical and economic impact of medication non-adherence indrug-susceptible tuberculosis: a systematic review. Int J Tuberc Lung Dis.
2020;24(8):811–9.
Liu Q, Lu P, Shen Y, Li C, Wang J, Zhu L, et al. Collateral Impact of theCoronavirus Disease 2019 (COVID-19) Pandemic on Tuberculosis Control
in Jiangsu Province. China Clin Infect Dis. 2021;73(3):542–4.
Mohammed H, Oljira L, Roba KT, Yimer G, Fekadu A, Manyazewal T. Containment of COVID-19 in Ethiopia and implications for tuberculosis careand research. Infect Dis Poverty. 2020;9(1):131.
Arega B, Negesso A, Taye B, Weldeyohhans G, Bewket B, Negussie T,
et al. Impact of COVID-19 pandemic on TB prevention and care in Addis
Ababa, Ethiopia: a retrospective database study. BMJ Open. 2022;12(2):e053290.
Thekkur P, Takarinda KC, Timire C, Sandy C, Apollo T, Kumar AMV, et al.
Operational Research to Assess the Real-Time Impact of COVID-19 on TB
and HIV Services: The Experience and Response from Health Facilities inHarare, Zimbabwe. Trop Med Infect Dis. 2021;6(2):94.
The Union. Mid-term report on impact of COVID-19 on TB and HIV. 2020.
Available from: https://​theun​ion.​org/​news/​the-​union-​shares-​mid-​term-​
report-​on-​impact-​of-​covid-​19-​on-​people-​with-​tb-​and-​hivai​ds-​in-​africa
Masina HV, Lin IF, Chien LY. The Impact of the COVID-19 Pandemic on
Tuberculosis Case Notification and Treatment Outcomes in Eswatini. Int J
Public Health. 2022;67:1605225.
Garg K, Bahurupi Y, Aggarwal P, Badola M. Impact of COVID-19 on the
National Tuberculosis Elimination Program in Uttarakhand, India: A
Mixed-Methods Research Study. Infect Prev Pract. 2023;5(1): 100269.
Jamwal A, Mohanty A, Swami A, Turbadkar D. Impact of COVID-19 on
tuberculosis services: an experience from a tertiary care hospital. J Med
Microbiol. 2022;71(6). Available from: https://​www.​micro​biolo​gyres​earch.​
org/​conte​nt/​journ​al/​jmm/​10.​1099/​jmm.0.​001559
Gandhi AP, Kathirvel S, Rehman T. Effect of COVID-19 lockdown on thepathway of care and treatment outcome among patients with tuberculosis in a rural part of northern India: a community-based study. J Rural
Med. 2022;17(2):59–66.
Gabdullina M, Maes EF, Horth RZ, Dzhazybekova P, Amanova GN, Zikriyarova S, et al. COVID-19 pandemic and other factors associated withunfavorable tuberculosis treatment outcomes—Almaty, Kazakhstan,
2018–2021. Front Public Health. 2023;11:1247661.
Andia-Biraro I, Baluku JB, Olum R, Bongomin F, Kyazze AP, Ninsiima S, et al.
Effect of COVID-19 pandemic on inpatient service utilization and patient
outcomes in Uganda. Sci Rep. 2023;13(1):9693.
Muñiz-Salazar R, Le T, Cuevas-Mota J, González-Fagoaga JE, ZapataGaribay R, Ruiz-Tamayo PS, et al. Impact of COVID-19 on tuberculosis
detection and treatment in Baja California. México Front Public Health.
2022;10: 921596.
Mbithi I, Thekkur P, Chakaya JM, Onyango E, Owiti P, Njeri NC, et al. Assessing the Real-Time Impact of COVID-19 on TB and HIV Services: The Experience and Response from Selected Health Facilities in Nairobi, Kenya. Trop
Med Infect Dis. 2021;6(2):74.
Thekkur P, Tweya H, Phiri S, Mpunga J, Kalua T, Kumar AMV, et al. Assessing
the Impact of COVID-19 on TB and HIV Programme Services in Selected
Health Facilities in Lilongwe, Malawi: Operational Research in Real Time.
Trop Med Infect Dis. 2021;6(2):81.
Lakoh S, Jiba DF, Baldeh M, Adekanmbi O, Barrie U, Seisay AL, et al. Impact
of COVID-19 on Tuberculosis Case Detection and Treatment Outcomes in
Sierra Leone. Trop Med Infect Dis. 2021;6(3):154.
Lestari T, Kamaludin, Lowbridge C, Kenangalem E, Poespoprodjo JR,
Graham SM, et al. Impacts of tuberculosis services strengthening andthe COVID-19 pandemic on case detection and treatment outcomes in
Mimika District, Papua, Indonesia: 2014–2021. Eslava-Schmalbach JH,
editor. PLOS Glob Public Health. 2022;2(9):e0001114.

Page 9 of 10

24. Andom AT, Fejfar D, Yuen CM, Ndayizigiye M, Mugunga JC, Mukherjee JS.
The Impact of COVID-19 on Tuberculosis Program Performance in theKingdom of Lesotho. Trop Med Infect Dis. 2023;8(3):165.
25. Surendra H, Elyazar IRF, Puspaningrum E, Darmawan D, Pakasi TT, Lukitosari E, et al. Impact of the COVID-19 pandemic on tuberculosis control inIndonesia: a nationwide longitudinal analysis of programme data. Lancet
Glob Health. 2023;11(9):e1412–21.
26. Cardenas-Escalante J, Fernandez-Saucedo J, Cubas WS. Impacto dela pandemia por COVID-19 en la tuberculosis en el Perú: ¿nos estamos olvidando de alguien? Enfermedades Infecc Microbiol Clínica.
2022;40(1):46–7.
27. Berra TZ, Ramos ACV, Alves YM, Tavares RBV, Tartaro AF, Nascimento MCD,et al. Impact of COVID-19 on Tuberculosis Indicators in Brazil: A Time
Series and Spatial Analysis Study. Trop Med Infect Dis. 2022;7(9):247.
28. Hasan T, Nguyen VN, Nguyen HB, Nguyen TA, Le HTT, Pham CD, et al. Retrospective Cohort Study of Effects of the COVID-19 Pandemic on Tuberculosis Notifications, Vietnam, 2020. Emerg Infect Dis. 2022;28(3):684–92.
29. World Health Organization. Global tuberculosis programme data. 2023.
Available from: https://​www.​who.​int/​teams/​global-​tuber​culos​is-​progr​
amme/​data
30. World Health Organization. WHO global lists of high-burden countries for
TB, HIV-associated TB and drug-resistant TB. 2021. Available from: https://​www.​who.​int/​news/​item/​17-​06-​2021-​who-​relea​ses-​new-​global-​lists-​of-​high-​burden-​count​ries-​for-​tb-​hiv-​assoc​iated-​tb-​and-​drug-​resis​tant-​tb
31. R Core Team. R: A Language and Environment for Statistical Computing.
Vienna, Austria: R Foundation for Statistical Computing; 2023. Available
from: https://​www.R-​proje​ct.​org/
32. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al.
Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686.
33. Dowle M, Srinivasan A. data.table: Extension of “data.frame.” 2023. Available from: https://​CRAN.R-​proje​ct.​org/​packa​ge=​data.​table
34. Ewing M. mgsub: Safe, Multiple, Simultaneous String Substitution. 2021.
Available from: https://​CRAN.R-​proje​ct.​org/​packa​ge=​mgsub
35. Zhu H. kableExtra: Construct Complex Table with “kable” and Pipe Syntax.
2021. Available from: https://​CRAN.R-​proje​ct.​org/​packa​ge=​kable​Extra
36. Venables WN, Ripley BD, Venables WN. Modern applied statistics with S.
4th ed. New York: Springer; 2002. 495 p. (Statistics and computing).
37. Canty A, Ripley B. boot: Bootstrap R (S-Plus) Functions. R package version
1.3-28.1. 2022.
38. Davison AC, Hinkley DV. Bootstrap methods and their application. Cambridge ; New York, NY, USA: Cambridge University Press; 1997. 582 p.
39. Fox J, Weisberg S. An {R} Companion to Applied Regression. Third. Sage;
2019. Available from: https://​socia​lscie​nces.​mcmas​ter.​ca/​jfox/​Books/​Compa​nion/
40. Fox J, Hong J. Effect Displays in R for Multinomial and Proportional-Odds
Logit Models: Extensions to the effects Package. J Stat Softw. 2009;32(1).
Available from: http://​www.​jstat​soft.​org/​v32/​i01/
41. Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with
R: a practical tutorial. Evid Based Ment Health. 2019;22(4):153–60.
42. Auguie B. gridExtra: Miscellaneous Functions for “Grid” Graphics. 2017.
Available from: https://​CRAN.R-​proje​ct.​org/​packa​ge=​gridE​xtra
43. Wilke CO. cowplot: Streamlined Plot Theme and Plot Annotations for“ggplot2.” 2023. Available from: https://​CRAN.R-​proje​ct.​org/​packa​ge=​
cowpl​ot
44. Slowikowski K. ggrepel: Automatically Position Non-Overlapping Text
Labels with “ggplot2.” 2023. Available from: https://​CRAN.R-​proje​ct.​org/​
packa​ge=​ggrep​el
45. McQuaid CF, Henrion MYR, Burke RM, MacPherson P, Nzawa-Soko R,
Horton KC. Inequalities in the impact of COVID-19-associated disruptions
on tuberculosis diagnosis by age and sex in 45 high TB burden countries.
BMC Med. 2022;20(1):432.
46. The State of Food Security and Nutrition in the World 2023. FAO; IFAD;
UNICEF; WFP; WHO; 2023. Available from: http://​www.​fao.​org/​docum​ents/​card/​en/c/​cc301​7en
47. United Nations. UN report finds COVID-19 is reversing decades ofprogress on poverty, healthcare and education. Available from: https://​www.​un.​org/​nl/​desa/​un-​report-​finds-​covid-​19-​rever​sing-​decad​es-​progr​
ess-​pover​ty-​healt​hcare-​and
48. Calina D, Hartung T, Mardare I, Mitroi M, Poulas K, Tsatsakis A, et al. COVID19 pandemic and alcohol consumption: Impacts and interconnections.
Toxicol Rep. 2021;8:529–35.

Gunsaru et al. BMC Medicine(2024) 22:312

49. Franke MA, Truß LM, Wierenga H, Nordmann K, Frühauf A, Ranaivoson R,
et al. Facilitators and barriers to TB care during the COVID-19 pandemic.
Public Health Action. 2022;12(4):174–9.
50. Stop TB Partnership. Information Note: Digital Health Technologies, Virtual
Care and Community-Based Monitoring Solutions for TB Programmes.
Available from: https://​www.​stoptb.​org/​covid​19
51. Zimmer AJ, Heitkamp P, Malar J, Dantas C, O’Brien K, Pandita A, et al.
Facility-based directly observed therapy (DOT) for tuberculosis during
COVID-19: A community perspective. J Clin Tuberc Mycobact Dis.
2021;24: 100248.

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